Software Defined Networking (SDN) is an important paradigm shift of computer networking in the last 10 years. The concept of SDN is so powerful that the potential of applying it can easily be perceived beyond the initial use case of large data centre networks. We are motivated by this perception to explore the potential use of SDN in the context of home networks specifically, even though home environments were not the driving scenario behind SDN in the first years of its development. Lacking other reviews on the subject, we performed a focused search for every article that proposes, discusses or otherwise addresses the idea of implementing SDN in home networking. We surveyed four major technical and online databases (IEEE Xplore, ACM, ScienceDirect and Wiley) to ensure the inclusion of relevant, quality and authentic works. The final filtered set included 42 articles that spanned the period from 2010 to 2017. Most of the articles address specific aspects of controlling and managing home networks, such as Quality of Experience, security, Internet caps, Internet-of-Things device management and other specific themes, while the rest of articles address the generic case of managing home networks using SDN without a special focus on a particular target application. We derive a simple taxonomy for the works on home SDN and summarize the complete set of works, highlighting few points along the way and drawing few simple statistics.

Positioning systems are one of the key elements required by location-based services (LBS). As global positioning system (GPS) was never intended for indoor environments, indoor positioning systems based on wireless local area networks (WLAN) have been proposed as a viable solution. RADAR algorithm is one of the famous indoor positioning techniques. It uses a database of predetermined fingerprints for selected locations to estimate the location of a mobile user in the signal space. Another new proposed algorithm named the Enhanced Fingerprint (EFP) uses the same principle as RADAR algorithm but with more than one fingerprint per location. In this paper we investigated the location fingerprint based algorithms by taking RADAR and EFP with more details. We evaluated these two algorithms in terms of accuracy, reliability and computation time. Experiment results show that the EFP algorithm gains 1.82 m of accuracy over RADAR algorithm, while the last one saves 75% of computation time. We found that EFP algorithm is more consistent with varying number of training samples.